Classification, Detection And Consequences Of Data Error: Evidence From The Human Development Index
The authors measure and examine data error in health, education and income statistics used to construct the Human Development Index. They identify three sources of data error which are due to data updating, formula revisions and thresholds to classify a country's development status. They propose a simple statistical framework to calculate country specific measures of data uncertainty and investigate how data error biases rank assignments. They find that up to 34% of countries are misclassified and, by replicating prior studies, they show that key estimated parameters vary by up to 100% due to data error.